from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Union[str, Any]     					=		'''EncodecFeatureExtractor'''
			__a    :					Tuple     					=		('''T5Tokenizer''', '''T5TokenizerFast''')





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				self.feature_extractor
							__lowercase					     =				False





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=True		)	->   Union[str, Any]:

							'''simple docstring'''


							return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__ ,  language=lowerCAmelCase__ ,  no_timestamps=lowerCAmelCase__		)





			def __call__(  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							if self._in_target_context_manager:
											return self.current_processor(*lowerCAmelCase__ ,  **lowerCAmelCase__		)

							__lowercase					     =				kwargs.pop('''audio''' ,  lowerCAmelCase__		)
							__lowercase					     =				kwargs.pop('''sampling_rate''' ,  lowerCAmelCase__		)
							__lowercase					     =				kwargs.pop('''text''' ,  lowerCAmelCase__		)
							if len(lowerCAmelCase__		) > 0:
											__lowercase					     =				args[0]
											__lowercase					     =				args[1:]

							if audio is None and text is None:
											raise ValueError('''You need to specify either an `audio` or `text` input to process.'''		)

							if text is not None:
											__lowercase					     =				self.tokenizer(lowerCAmelCase__ ,  **lowerCAmelCase__		)

							if audio is not None:
											__lowercase					     =				self.feature_extractor(lowerCAmelCase__ ,  *lowerCAmelCase__ ,  sampling_rate=lowerCAmelCase__ ,  **lowerCAmelCase__		)

							if audio is None:
											return inputs

							elif text is None:
											return audio_inputs

							else:
											__lowercase					     =				audio_inputs['''input_values''']
											if "padding_mask" in audio_inputs:
															__lowercase					     =				audio_inputs['''padding_mask''']
											return inputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				kwargs.pop('''audio''' ,  lowerCAmelCase__		)
							__lowercase					     =				kwargs.pop('''padding_mask''' ,  lowerCAmelCase__		)

							if len(lowerCAmelCase__		) > 0:
											__lowercase					     =				args[0]
											__lowercase					     =				args[1:]

							if audio_values is not None:
											return self._decode_audio(lowerCAmelCase__ ,  padding_mask=lowerCAmelCase__		)
							else:
											return self.tokenizer.batch_decode(*lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							return self.tokenizer.decode(*lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[np.ndarray]:

							'''simple docstring'''


							__lowercase					     =				to_numpy(lowerCAmelCase__		)
							__lowercase     ,	__lowercase     ,	__lowercase					     =				audio_values.shape

							if padding_mask is None:
											return list(lowerCAmelCase__		)

							__lowercase					     =				to_numpy(lowerCAmelCase__		)

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							__lowercase					     =				seq_len - padding_mask.shape[-1]
							__lowercase					     =				1 - self.feature_extractor.padding_value
							__lowercase					     =				np.pad(lowerCAmelCase__ ,  ((0, 0), (0, difference)) ,  '''constant''' ,  constant_values=lowerCAmelCase__		)

							__lowercase					     =				audio_values.tolist()
							for i in range(lowerCAmelCase__		):
											__lowercase					     =				np.asarray(audio_values[i]		)[
											    padding_mask[i][None, :] != self.feature_extractor.padding_value
											]
											__lowercase					     =				sliced_audio.reshape(lowerCAmelCase__ ,  -1		)

							return audio_values
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)